Screening Procedure for Pediatric Head Trauma

Efficiently triaging patients with Optimal Classification Trees

Detecting traumatic brain injury

Most head trauma in children appears to be minor, and therefore it is challenging to identify whether a child presenting at an emergency department has a traumatic brain injury that requires immediate intervention.

CT scans provide a precise diagnosis of whether intervention is required, but are costly, can require sedation, and result in exposure to radiation (which has heightened risk of adverse effects for children).

Fewer than 1% of patients require intervention, motivating a screening procedure that recommends which patients should receive CT scans. The goal is to minimize the number of needless scans without missing any case that requires intervention.

The PECARN rules are a widely-used screening procedure based on CART models. They have a very low false-negative rate, but require CT scans for up to 45% of all patients.

PECARN rules for screening pediatric head trauma

Effective screening with Optimal Trees

We created a modern screening procedure by training Optimal Classification Trees on the same dataset that was used to derive the PECARN rules.
  • Improved precision

    Only requires CT scans for 30% of patients while maintaining the same low false-negative rate.

  • Clinically validated and trusted

    The transparency and interpretability of the tree models enabled physicians to inspect the decision logic.

  • Accessible and intuitive

    Delivered as a simple interactive calculator, enabling easy access to the improved model.

"The potential to revolutionize clinical decision making"

Our analysis was published in JAMA Pediatrics, the leading journal for pediatric care.

The editorial published alongside the article commended the transparency of the model, emphasizing that "it may be difficult for clinicians to counsel patients about the implications of a rule that is perceived as a black box or ghost in the machine".

Unique Advantage

Why is the Interpretable AI solution unique?

  • Power of Optimal Trees

    33% reduction in needless CT scans, resulting in lower costs and fewer children subjected to radiation

  • Trusted and validated

    Interpretability enabled efficient collaboration with domain experts, resulting in an extensively validated model that has earned trust

  • End-user familiarity

    The existing rules are also tree-based, so the new model can be rolled out with little learning curve

  • Ease of access

    The simplicity of the tree logic enables delivery as a calculator that runs entirely on the end-user device

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